Research Insights
Multi-Model Forecasting of Influenza Seasonal Dynamics to Increase National and Global Forecasting Accuracy and Capacity
Prior to the emergence of the COVID-19 pandemic, influenza viruses led to an estimated 140,000-170,000 hospitalizations and 12,000-52,000 annual deaths in the US. In response to the global spread of COVID-19, governments intermittently enacted strict non-pharmaceutical interventions (NPIs), including school closures, stay-at-home orders, targeted business capacity restrictions or closures, and mask mandates, which in combination suppressed the 2020-2022 influenza seasons in both the Southern and Northern Hemispheres. However, the 2022-23 influenza season returned to the same levels of hospitalizations compared to the Pre-COVID seasons. In the future 2024-25 season, it remains uncertain the extent to which specific NPIs after the COVID-19 pandemic reduced influenza transmission in isolation, in combination, and across diverse socioeconomic and health subgroups.
The UT COVID-19 Modeling Consortium (UT-CMC) has extensive experience in modeling the detection, transmission, and control of influenza and has collaborated closely with local, state, and federal agencies to improve seasonal and pandemic influenza forecasting, surveillance, and intervention strategies. Our models have elucidated the complex interplay between influenza transmission, human behavior, viral evolution, and public health interventions. We have also developed practical decision-support tools for the Centers for Disease Control (CDC), Defense Threat Reduction Agency (DTRA), Association of Public Health Labs (APHL), and Texas Department of State Health Services (DSHS) to accelerate the detection of emerging influenza outbreaks, improve the forecasting of ongoing epidemics, and ensure the fair and effective use of limited mitigation resources including ventilators, antiviral drugs, and vaccines. We propose to improve influenza forecasting projection models. We will provide technical guidance as the team uses the models to (1) produce short-term probabilistic influenza forecasts, and (2) broadly contribute to influenza forecasting and scenario modeling hub efforts across the CSTE and CDC Network. We will participate in regular on-line collaborative meetings, provide high level guidance on achieving the aims of the grant, and contribute to disseminating results through progress reports and publications.
Funder: Council of State & Territorial EPI
Amount: $300,000
PI: Spencer Fox, College of Public Health